A reliability evaluation method for distribution networks considering passive islanding detection failure

Microgrids with large distribution networks require an adaptive protection system due to their complex nature of fault behavior and different modes of operation. Conventionally, the adaptive protection in microgrids is realized through one of the passive islanding detection methods due to the ease of implementation and economy. The main drawback of the passive islanding detection methods is the specific non-detection zone (NDZ) associated with each method. To address these gaps, this paper proposes an adaptive protection scheme based on a hybrid method that detects microgrid islanding considering the dynamic operating behavior of the microgrid system, instantaneous current measurement and fault recovery behavior. The proposed adaptive protection scheme allows for timely detection of microgrid islanding, auto-tuning of relay settings while increasing the overall NDZ compared to the existing methods. Moreover, the proposed scheme is fully autonomous and independent of communication links. The effectiveness of the proposed adaptive protection scheme has been thoroughly investigated in DIgSILENT PowerFactory software for a wide-range of practical operating scenarios.

Islanding phenomenon is often undesirable for microgrids because it may interrupt power services, and cause damage to the equipment. At times, islanding is also required to isolate any potential problem. Anti-islanding methods can detect the islanding, causing the distributed generators (DGs) to stop providing power to the loads in microgrids, the passive islanding techniques are easy implementation, but the non-detection zone (NDZ) of them is too large. This paper presents a method to reduce the NDZ of passive islanding techniques by controlling the output power of the inverters. A structure of hybrid AC/DC microgrid is under operating with the anti-islanding method. The over/under voltage (OUV) and over/under frequency (OUF) methods are used to detect the islanding phenomenon at the point of common coupling (PCC) to ensure all power generated by DGs can be fully utilized. The proposed system is developed in MATLAB/SIMULINK environment and tested for the results.

The existing passive methods for islanding detection are mainly based on the detection of voltage and frequency deviation after islanding, using protections such as voltage vector shift (VVS) and rate of change of frequency (ROCOF). Although there are reported issues with these passive methods such as inherent non-detection zones and nuisance trips, utilities prefer the passive methods due to the low cost and simplicity of deployment. In this paper, one composite passive islanding detection method is presented. It tracks the voltage phase angle, the system frequency, and ROCOF every power cycle. If three phase voltage vectors shift in the same direction and the rotated angle values are balanced, the calculation of the accumulated phase angle drift (PAD) will be initiated. This calculation continues until the ROCOF measurement is below the ROCOF setting threshold. If the accumulated phase angle drift reaches the set angle threshold, the condition for islanding is claimed. The performance of this composite method is verified under different scenarios based on Matlab Simscape multidomain physical systems and practical waveforms recorded from sites. Although there are still non-detection zones, this composite PAD solution has better sensitivity than existing VVS and ROCOF methods and is stable under external system faults.

A new passive islanding detection technique based on transient energy that is measured and analysed at targeted distributed generation (DG) or the point of common coupling (PCC) is presented. The proposed technique measures the superimposed components and then calculates the transient active energy (TAE) and the transient reactive energy (TRE). Using the relation between TAE and the TRE, the relay can determine the islanding situation even in a worst‐case operational scenario, and this is a notably shorter response time according to the current standards. The performance of the proposed technique is simulated in the MATLAB environment. Islanding and non‐islanding scenarios for different power match conditions and types of DGs are simulated. The evaluation of the simulation results shows that the proposed technique identifies the islanding with a less than zero power mismatch, operates accurately under the condition of load variation, and does not conflict with the power system operation during normal conditions. It removes the passive technique drawbacks by having a tiny non‐detection zone (NDZ) and a faster response.

Islanding is a major problem that is related to a grid connected distribution generator. During islanding the distribution generator gets disconnected from the grid while still being energized. It should be detected as fast as possible in order to prevent the distribution generator from getting damaged or to ensure the safety of the personnel working. The islanding detection makes use of system parameter to detect islanding at the time of occurrence. In this paper a passive islanding detection method based on ripple content of voltage at the point of common coupling is being used to differentiate between islanding and non-islanding conditions. A threshold level is set upon the ripple content of voltage at the point of common coupling and islanding is detected whenever the threshold level is exceeded for some certain period of time. This method effectively distinguishes the islanding condition from other non-islanding condition and detects the islanding condition efficiently. The working of this method is being ensured by simulating a variety of islanding and non-islanding condition. No non-detection zone was shown by this method and islanding conditions were detected within 150ms of the allowed delay time. Theoretical analysis and simulation results are presented to validate the proposed method.

One of the facing problems in distributed generation is being unintentionally islanded which the detection of this phenomenon is of great importance. Common methods are active and passive islanding detection. These two methods are based on changing in parameters such as frequency, voltage and current harmonics. If, after the islanding occur, this changes are revealing and visible, islanding phenomenon is demonstrated which is detected by passive methods. If the generated power of inverter is equal to local loads consumption and assuming of the generated reactive power of grid connected inverter equal to zero, it should be detected by active methods but these two methods have some challenges such as reduction in power quality or wide Non Detection Zone (NDZ). In this paper, in proposed method, change of a new parameter will be studied for islanding detection diagnosis. With the difference that the studied system is a three phase system and unbalanced load is studied which is more consistent to reality. Because of small changes in parameters, we have to find the right approach toward active methods. Generally there are two views about the islanding performance. At first view, this phenomenon is seems negatively but in fact this is an opportunity. The first view suggests that all generators in the island should bring out of grid within a maximum of two seconds. In this view the concept of anti-islanding protection is proposed to tackle the task of creating such islands. The second view expresses the concept of micro grid. In this case as far as possible, power required by Inner Island loads were provided by distributed generators. The studied system was similar to standard IEEE-1547 and UL-1741. With the difference that we used three phase and local loads are assumed unbalanced. Conventional active detection methods are not able to identify the island with the aim to help keep it stable. They usually try to bring out the electrical parameters such as voltage and frequency from nominal limits. So in micro grids the island should be diagnosed using passive or remote techniques. Note that detection time of 2 seconds which is referred to on standards is not good and the detection time must be lower than this value. However, the proposed method that is one of the active detection methods is capable for use in micro grids. Simulation results using PSIM software on standard network shows the effectiveness of the proposed method. Keywords: Islanding, Unbalanced Loads, dq-frame, Phase Locked Loop (PLL), Non Detection Zone (NDZ), Micro-grid.

An active islanding detection technique for an inverter-based distributed generator (DG) is presented. Two control strategies are studied: one in which the DG only supplies active power and one in which the DG supplies both active and reactive power to improve the power factor. The nondetection zone (NDZ) of the over/under voltage and frequency protection (OVP/UVP and OFP/UFP) method for each control strategy is determined. For the presented interface control, it is observed that the NDZ of the OVP/UVP and OFP/UFP is larger when the DG interface is designed to improve the power factor. In order to decrease the NDZ, an islanding detection method is designed that is based on creating a mismatch between the load and DG reactive power, thus forcing the frequency to deviate outside the OFP/UFP limits. It is shown that the proposed islanding detection technique is robust and has a negligible NDZ.

The increased requirement for clean, cost effective and uninterrupted electricity has led to the incorporation of microgrid including distributed generation (DG) units. One of the major issue with DG interconnection is islanding detection. This paper presents the comparative assessment report of islanding detection techniques on the basis of their non detection zone (NDZ) and detection time. A comprehensive table comparing the different available islanding detection methods is also presented.

This article presents a Radial Basis Neural Network (RBNN) based islanding detection technique. Islanding detection and prevention is a mandatory requirement for grid-connected distributed generation (DG) systems. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large non detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The proposed algorithm is compared with the widely used rate of change of frequency relays (ROCOF) and found working effectively in the situations where ROCOF fails. This approach utilizes rate of change of frequency at the target distributed generation location and fed to the radial basis neural network for intelligent islanding detection. Hence a better reliability is provided. This approach utilizes the artificial neural network (ANN) as a machine learning technology for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loading, switching operation, and network conditions. Simulation studies showed that the RBNN-based algorithm detects islanding situation accurate than other islanding detection algorithms. Moreover, for those regions which are in need of a better visualization, the proposed approach would serve as an efficient aid such that the mains power disconnection can be better distinguished

ABSTRACTImproved efficiency, enhanced integration with distributed generations (DGs), and the proliferation of DC loads in power systems have driven a surge in the adoption of DC microgrids (DCMGs). However, unintentional islanding poses a significant threat to these microgrids, necessitating robust islanding detection techniques to safeguard personnel, equipment and system stability. This paper proposes two novel passive islanding detection methods for DCMGs, explicitly addressing the non‐detection zone (NDZ). The first proposed method involves collecting voltage samples at the DG terminals and computing their empirical mode decomposition (EMD). Subsequently, intrinsic mode functions (IMFs) are extracted, and the energy in each window is calculated to establish a diagnosis. To effectively distinguish islanding from other disturbances and faults, various scenarios are investigated. The second method is predicated on ensemble empirical mode decomposition (EEMD). Following the collection of voltage samples from DG terminals, white noise is introduced to these samples. The method subsequently extracts IMFs through averaging and quantifies the energy content within each window. The proposed methods are validated through simulations in Simulink/MATLAB. The obtained results demonstrate the methods’ effectiveness in achieving fast and accurate islanding detection.

This paper presents results of studies of the impacts of substation monitoring on reliability indices of distribution systems. It was detected that the fault detecting and repair time was the main cause of the prolonged outages experienced in the system which increases the poor reliability indices of the system. The intelligent monitoring device detect open circuit faults and relay to the base station in 30s compared with 3 to 24 hours it was taking to detect fault. The intelligent substation monitoring device reduced the fault detection time and hence improves the reliability of distribution systems. The system was simulated, and result showed that SAIDI was reduced from 244 hours of outage per annum to just 98 hours of outage per annum while CAIDI was improved from 10 hours of interruption per outage to 5 hours of interruption per outage. This paper demonstrated that the fault detection and reporting time can be greatly reduced by the use of intelligent monitoring system and reliability indices.

This paper describes an analytical technique and a Monte Carlo simulation approach for determining the reliability indices of distribution systems. The proposed analytical method is based on the 'device of stages' technique so that the impact of equipment aging can be incorporated into the model. Two types of stage configurations are described. For each configuration, the parameters of their probability density functions are estimated using the method of matching moments. The Monte Carlo simulation approach utilizes the component state duration sampling method. A computer program has been developed to implement these methods. The application of the concepts presented in the paper are illustrated by the analysis of a practical configuration.

Distributed generators (DGs) require detection of islanding condition due to personnel safety hazards and equipment damages. Anti-islanding methods are divided into passive and active methods. The passive methods are based on measurement of the natural effects of islanding. The active methods use the effects by intentional transients or harmonics. When the power generated by the DG matches power consumption by load, passive methods fail due to small natural effect of islanding. Therefore, the passive methods have a non-detection zone (NDZ). The active methods can reduce the NDZ size. But these methods reduce the power quality of the grid. In this paper, a novel anti-islanding method (AIM) is proposed. The single phase DG using the proposed AIM injects output current included a little harmonic current into the grid and monitors the harmonic component of voltage at the point of common coupling (PCC) by using Goertzel algorithm. The Goertzel algorithm is a kind of discrete Fourier transform (DFT). It extracts magnitude and phase of the desired frequency from the input signal, with minimum computation. The proposed islanding detection algorithm does not only have NDZ but also bad effect on the power quality of the grid due to injecting harmonic components qualified the interconnection standard. The proposed islanding detection method (IDM) is verified by the PSIM simulations and the experimental results.

The increasing penetration of distributed generation (DG) units into power system networks has increased the overall reliability of global electricity consumption. DG integration also creates some challenges in the form of unintentional islanding, power quality, and bi-directional power flows. Islanding of DG units creates safety issues for the maintenance personnel and can damage protection equipment. The paper presents a passive islanding detection approach (IDA), which is based on voltage harmonics, extracted using a Kalman Filter (KF) model. A KF model is utilized to extract the harmonic features from the measured modal voltage (MV) signal at DG terminals. A derivative of the selected voltage harmonic distortion (SVHD) is considered as a decisive factor for islanding detection. The proposed IDA is validated using UL 1741 standard test cases, which confirm the reliability and efficiency of the proposed approach. The simulations are performed in the Matlab/Simulink environment. The results demonstrate that the proposed approach can detect islanding condition under worst operating scenarios and has a negligible non-detection zone (NDZ).

Distributed Generation (DG) sources predominantly renewable energy-based like solar photovoltaic and wind energy sources are considerably penetrated in power system networks. This high penetration of DG sources may lead to the formation of unintentional islands which is hazardous to both the equipment and personnel, if sustained. In this paper, 46 passive parameters are considered to find out which candidate(s) are promising for detecting sustained unintentional islands and avoid false DG trips. These 46 parameters are derived from point of common coupling (PCC) voltage, DG output current, frequency, power factor angle, active and reactive powers. All these parameters are tested on standard IEEE 13 and 34 bus distribution networks integrated with inverter-interfaced DGs at different locations for different penetration levels in MATLAB/SIMULINK environment. The parameters are tested for different islanding events (comprising of various local loads such as conventional parallel RLC resonant load, R, RC, and RL loads) and for different non-islanding events. The parameters are then ranked accordingly by a suitable averaging approach based performance ranking technique. From this analysis, the best candidates are obtained for detecting inverter DG islands at single-point (or single PCC) location and multi-point (or multiple PCCs) locations. Multiple best passive candidates are obtained for different island scenarios from which a set of promising islanding detection indicators are proposed.